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PhD Candidate @ Georgia Tech / UW | PhD Research Intern @ Ai2 | NSF & Google PhD Fellow
I'm an ML PhD Candidate at Georgia Tech co-advised by Professors Judy Hoffman (fmr. Georgia Tech now UCI), Ranjay Krishna (UW) and Ali Farhadi (UW). I'm also actively involved in research at Ai2. My work focuses on efficient model development, from increasing training efficiency via improved data quality and objectives, to developing lightweight post-training methods. I'm grateful to be supported by both a Google PhD Fellowship and an NSF Graduate Research Fellowship.
Georgia Institute of Technology
Doctor of Philosophy - PhD, Machine Learning
August 1, 2021 – May 1, 2027
Carnegie Mellon University
Bachelor’s Degree, Statistics and Machine Learning
January 1, 2015 – January 1, 2019
The College Preparatory School
High School
January 1, 2011 – January 1, 2015
Ai2
PhD Research Intern
June 1, 2026 – Present
Seattle, Washington, United States · On-site
Ai2
Student Researcher
June 1, 2025 – June 1, 2026
Seattle, Washington, United States · On-site
Paul G. Allen School of Computer Science & Engineering
Visiting PHD Student
September 1, 2024 – Present
Seattle, Washington, United States
Google DeepMind
Student Researcher
April 1, 2024 – August 1, 2024
Mountain View, California, United States · On-site
Georgia Institute of Technology
Graduate Research Fellow
August 1, 2023 – Present
Georgia Institute of Technology
Graduate Research Assistant
August 1, 2021 – August 1, 2023
Machine Learning Department at CMU
Research Assistant
August 1, 2019 – January 1, 2021
Inokyo
Machine Learning Researcher
June 1, 2019 – August 1, 2019
San Francisco, California
Machine Learning Department at CMU
Research & SCS Honors Thesis
May 1, 2018 – May 1, 2019
Palo Alto Networks
Data Science Intern
May 1, 2018 – August 1, 2018
Santa Clara, California
Machine Learning Department at CMU
Teaching Assistant - 10-701 Machine Learning (PhD)
January 1, 2018 – May 1, 2018
Conviva
Engineering Intern
May 1, 2017 – July 1, 2017
Foster City, CA
Upwork
Data Science Intern
May 1, 2016 – July 1, 2016
Mountain View, CA
edX Verified Certificate for Introduction to Computer Science and Programming Using Python
edX
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a research-intensive ML Engineer role, given their extensive academic and industry research experience. Their diverse project portfolio, ranging from theoretical advancements in flow matching and model merging to practical applications in autonomous checkout and conversational agents, shows adaptability and a broad interest in the ML domain. The involvement in multiple prestigious institutions (CMU, Georgia Tech, Google DeepMind, Ai2) indicates a drive for excellence and collaboration within leading research environments.
Soft Skills & Operational Fit
The candidate's extensive research and academic background, including TA experience, suggests strong analytical, problem-solving, and communication skills. Their involvement in leading projects and publishing papers indicates initiative and the ability to work independently and collaboratively. The product-focused work at Google DeepMind also points to an understanding of practical application and business needs.